Vertical Coordination in Transition Countries:
A comparative study of agri-food chains in
Moldova, Armenia, Georgia, Russia, Ukraine
John White1 and Matthew Gorton2
1
2
Faculty of Social Science and Business, University of Plymouth, UK.
John.White@plymouth.ac.uk
School of Agriculture, Food and Rural Development, University of Newcastle,
UK. Matthew.Gorton@ncl.ac.uk
Version: September 2004
Report prepared for The World Bank (ECSSD) project on “Vertical Coordination
in ECA Agrifood Chains as an Engine of Private Sector Development:
Implications for Policy and Bank Operations” (Contract No.7615040/7620016)
Table of Contents
Part A: Introduction, Research Methodology and Summary of Key Findings .......... 3
Introduction ..................................................................................................................... 3
Methodology ................................................................................................................... 4
Key Findings ................................................................................................................... 5
Part B: Analysis of Interview Findings ........................................................................... 9
Sources of Supply ........................................................................................................... 9
Contract Assistance Measures ...................................................................................... 10
Foreign Investment ....................................................................................................... 11
Marginalization of Small Farmers? .............................................................................. 12
Product Quality ............................................................................................................. 14
Product Quality and Contracting .................................................................................. 15
First Mover Advantage and the Impact of Contracting ................................................ 16
FDI, Product Quality and Contracting .......................................................................... 18
Exporting and Relationships with Investment and Contracting ................................... 19
Future Developments .................................................................................................... 22
Qualitative Findings on Business Constraints and National Government and World
Bank Priorities .............................................................................................................. 23
Conclusions and Policy Recommendations .................................................................. 24
References ........................................................................................................................ 27
Appendix 1: Country Comparisons .............................................................................. 29
Appendix 2: Sector Comparisons .................................................................................. 32
Appendix 3: Dairy Industry ........................................................................................... 35
Appendix 4: Interview Questionnaire ........................................................................... 37
2
Part A: Introduction, Research Methodology and Summary of Key Findings
Introduction
Restructuring and privatisation in the ECA region has led to the separation of many
previously horizontally and vertically integrated enterprises together with the emergence
of de novo businesses. Enterprises have had to forge their own relationships with buyers
and suppliers in an environment of both weak public institutions for enforcing contractual
obligations and property rights, and a high level of macroeconomic instability. These
problems have been identified as impediments to growth with the dislocation to, and
failure of, inter-enterprise relationships being a causal factor in the falls in output
witnessed in the early years of transition (Blanchard and Kremer, 1997; Gow and
Swinnen, 2001).
With the break-up of former state and collective farms, established food processors in the
Former Soviet Union (FSU) have lost guaranteed, state directed, supplies and demand.
Food processors have had to establish their own relationships to effectively procure
agricultural raw materials. In meeting this challenge processing enterprises can source
farm level output through three main mechanisms: spot markets, vertical ownership
integration or contracting. Contracting appears to be the favored mechanism of many
large food and agribusiness companies in the region and the introduction of contracting
has been linked to significant improvements in productivity (Gow et al. 2000).
However, while case study evidence points to the potential role of contracting as an
engine for growth in agri-food supply chains there is a lack of systematic evidence on its
impact. Unresolved issues concerning the impact of contracting and vertical integration
on the FSU agri-food sector were identified in a recent World Bank concept paper on this
topic. The concept paper, which defined the parameters for this report, identifies five
main unresolved issues concerning:





Under what conditions do contract relationships emerge and what role does
government play?
What is the role of foreign direct investment (FDI)?
What triggers beneficial spillover effects of foreign investment and how general
are such developments?
Is there an optimal model of contracting or should contractual relationships vary
due to differences in markets and stages of transition?
What are the key equity issues and does the process of vertical contracting lead to
the exclusion of small farms?
This study sought to collect and analyze data from the ECA region to help answer these
questions, providing a basis for identifying options for improved policies and
investments.
3
Methodology
To investigate the issues outlined in the concept note, a standardized survey instrument
was designed to obtain data from agri-business enterprises. As the survey was concerned
with contracting and investment, purposive sampling (Lincoln and Guba, 1985) was
employed. Purposive sampling can be defined as the selection of cases ‘from which the
most can be learned’ (Merriam, 1998, p.61). Under this method, ‘sample elements are
handpicked because it is believed they can serve the research purpose’ (Churchill, 1999,
p.503). In this case, only interviewees that met certain criteria were selected. The criteria
chosen were:
a)
b)
c)
Senior executives of agri-food industry enterprises (excluding microenterprises and those that had just been established);
Enterprises had made recent capital investments in the agri-food sector;
Enterprises were engaged in contracting with farmers.
These criteria were designed to ensure that the sample contained companies that were
engaged in activities that the study sought to understand and evaluate. A quota of 12
companies per country was drawn up by researchers in each state, who checked that
potential interviewees met the criteria listed above. For each country a target of 4 milk
processors, 4 plant based enterprises (sugar, milling, fruits etc.) and 4 value-added
companies (reflecting products of national importance that varied between states such as
wine, brandy and speciality cheeses / ice cream) was set. This division was designed to
pick up on sub-sector differences and reflect the broad balance of the agri-food sectors in
the FSU.
The survey instrument contains both open and closed questions. Numerical data was
obtained on firm performance and background characteristics, the value of capital
investments, contract relationships with farmers, the impact of contracting, quality
standards and contract breaches. Open ended questions were designed to obtain
qualitative information on the rationale for investments, contract decisions and future
prospects.
Interviews were conducted in relevant national languages with responses translated into
English for cross-national analysis. Fieldwork was undertaken by: Naira Mkrtchyan and
Vahe Heboyan (Armenia), Alexander Didebulidze (Georgia), Mikhail Dumitrashko and
Anatolie Ignat (Moldova), Alexander Yermolov (Russia) and Alexander Skripnik
(Ukraine).
The sample of 60 enterprises collectively accounted for 18,556 employees in 2003 and
had a combined turnover of 215.6 million USD. The mean level of employment for the
sample was 309 full-time equivalents with an average turnover of just under 3.6 million
USD per annum (Table 1). The sample therefore incorporates some major players in the
FSU agri-food sector.
4
Table 1: Sample Characteristics by Country
Country
Armenia
Georgia
Moldova
Russia
Ukraine
Sample size
12
12
12
12
12
Mean employment (2003)
134
527
259
218
409
Mean turnover (2003)
3,305,602
1,460,057
3,678,057
1,808,042
7,712,667
60
309
3,592,885
Total
Key Findings
This section summarizes the key findings of the interviews. For each point, reference is
made to the relevant tables in Section B of the report.
1. Contracting between processors and farmers became more prevalent over the
period 1997 to 2003 in the cases studied. The use of contracts with both small and
large farmers grew but growth in the latter case has been greater. By 2003, more
enterprises had contracts with larger farms than smaller farms but the reverse was
true for sourcing from spot markets where relationships with small farmers are
more prevalent. The use of other agents such as traders and intermediaries as a
source of supply has also increased (Table 2).
2. As part of a contract a processor may provide support measures, such as credit,
physical inputs and technical advice, to farmers. 38.3 per cent of processors in the
sample offer credit to at least some of the farms that supply them. The
corresponding figure for physical inputs is 33 per cent. On average the processors
that offer credit and physical inputs do so to approximately one half of the farms
that supply them and around 60 per cent of processors have a minimum size of
farm below which they do not offer such support. Investment loans and
machinery are far less commonly granted to farms and, when they occur, are
offered on a more selective basis. Processors report that they rarely discriminate
against smaller farmers in providing agronomic support, guaranteed prices or
prompt payments (Table 3).
3. While several contract support measures are provided on a selective basis, overall
their use is spreading to a larger number of farms. Only in a minority of cases is
the mean percentage of farms to which a contract support measure is currently
offered, lower than in the first year that the measure was introduced by the
processor. The measures that are now more selectively offered are investment
loans, harvesting and handling support and prompt payments. The impact of the
first two measures has not been as successful as often hoped and the change in the
number of farms with access to the latter two support measures has been small
(Table 3)
5
4. Interviewees were asked to assess the mean impact of each contract support
measure employed on farm yields and quality of output (measured as the
percentage change in farm output reaching higher and basic standards as a result
of the contract support measure). The mean impact across all contract support
measures employed was a rise in farm yields by 9.1 per cent and an average
increase of 9.5 per cent in the amount of farm level output reaching higher [extra
class / premium class] standards. The impact on the amount of farm level output
reaching basic standards was less as most agricultural output was judged to be
reaching this level prior to the implementation of contract support. The measures
with the greatest impact on yields were veterinary support, physical inputs and
specialist storage (especially cooling equipment in the dairy sector) and a set of
market measures (prompt payments, guaranteed prices and market access). In
terms of raising quality, quality control support, veterinary support, physical
inputs, market access and prompt payments have had the greatest impact. The
returns to investment loans and machinery have been relatively poor (Table 3).
5. The majority of contract support measures have been introduced since 1999 and
for proactive reasons. Proactive reasons were classified as a motivation to
enhance product quality, improve competitive offerings and meet consumer
demand. There is no relationship between year of introduction and the impact on
yields or farm level quality. However the mean impact of contract support
measures introduced for proactive reasons has been significantly greater than
where measures have been introduced reactively (matching the support of
competitors, protecting supply base). This suggests that the impact of contracting
is not uniform or related to specific time-periods but is greatest for first-movers
(Tables 10 and 11).
6. By analyzing changes in the proportion of agricultural raw materials supplied to
processors that fell into premium, acceptable and rejected / unusable categories,
changes over time in the quality of farm level output can be assessed. The
majority of processors (61.7 per cent) saw an improvement in quality over the
period 1997-2003. 11.7 and 26.7 per cent saw no change or worse farm product
quality respectively. Those that saw an increase in product quality procured a
significantly greater proportion of agricultural raw materials using contracts and
also employed a significantly greater number of contract support measures
(Tables 7, 8 and 9).
7. Many have expressed a concern that the spread of contracting leads to the
marginalization of small farms. Marginalization can be looked at in two main
ways: the number of small farms dealt with and the terms and conditions of those
relationships. Regarding the first aspect, from the survey data there is little
systematic evidence of marginalization: only just over 1 in 5 processors (21.7 per
cent) reported that they were dealing with fewer small farms (defined as less than
1 hectare or 5 cows in the dairy sector) in 2003 than 1997. In contrast, 55 per cent
are dealing with more small farms although the share of total raw materials
6
sourced from small farms has fallen in just over one-third of processors sampled.
As demand has stabilized and increased in the FSU, processors have looked to
source more agricultural raw materials and small farms have not, in the vast
majority of cases, been excluded. However their terms and conditions as
evidenced by the selective granting of contract support measures may be worse
(see Table 3).
8. The results highlight more positive impacts of Foreign Direct Investment (FDI)
than negative. FDI-firms have made significantly greater capital investment (both
as a total and per employee). Most of this investment has been in upgrading
processing facilities. Western FDI-firms also employ a significantly greater
number of contract support measures and, maybe somewhat surprisingly, source a
significantly greater proportion of agricultural inputs from small farms. All but
one of the 13 firms that have reduced the number of small farms that they deal
with are owned by domestic private investors (Tables 4 and 12).
9. Exporting is associated strongly with FDI. As a result there is a high degree of
overlap between the characteristics of exporters and FDI-firms. Exporters have
made greater capital investments, source a greater proportion of the agricultural
raw materials from small farms and significantly less from other agents. Exporters
do employ a greater number of contract support measures (Table 13).
10. Few processors have specific plans to reduce the number of farmers they deal
with. About one third expect to be dealing with fewer farmers in future but this is
largely accounted for by farmer led initiatives (switching to different agricultural
activities and exit from small scale agriculture as macroeconomic prospects
recover) (Tables 14 and 15).
11. The most widespread problems faced by processors are ineffective or
inappropriate market governance, problems procuring agricultural raw materials
and meeting the challenge of the greater internationalization of markets.
Supporting internationalization was identified as an investment and policy priority
particularly as currently exporting is, with a few notable exceptions, largely
limited to FDI-firms. International technical assistance and advice on exporting
was seen as particularly important given that such specialist support typically
cannot be obtained from local educational establishments.
12. A breakdown of results by country and sector is provided in Appendices 2 and 3
respectively. When interpreting the results in the appendices the small size of subsamples should be noted. The number of contract support measures offered is
significantly higher in Armenia, Georgia and Moldova than Russia and Ukraine.
This may reflect the greater level of FDI in the Armenian, Georgian and
Moldovan samples, given the previously found link between FDI and contract
support (Tables 12 and 16). Moldova also has the most fragmented supply base
although some consolidation was witnessed in the period 2001-3.
7
13. Bearing in mind the small size of some sub-samples, contract support measures
are most widely used in the sugar sector (mean of 5.75 measures employed per
processor) and for wine / brandy. This may reflect how (a) sugar processors and
wineries are procuring directly from farmers rather than agents / distributors, (b)
FDI has been more significant in these sectors and (b) quality requirements are
more acute in these sectors. A noticeably low proportion of supply comes from
small farms in the sugar sector (6.3 per cent) although sugar refineries do deal
with a large number of small farms (mean of 1275 in 2003). In the wine / brandy
sector over three-quarters of grapes come from small farms and this may reflect
why so many wineries in the FSU wish to purchase vineyards to provide a more
stable supply of grapes that meets their quality requirements (Tables 18 and 19).
14. Analyzing only companies engaged in the dairy sector (Appendix 3) it is evident
that contracting is most extensively developed in Moldova and Armenia. This can
be discerned both in terms of the share of raw materials sourced using contracts
with farms and the mean number of contract support measures employed. In
Moldova this may reflect the higher level of Western-FDI and in Armenia a
relatively high proportion of dairy output is exported. These findings are in line
with the relationships between both Western FDI and exporting with contracting
found for the full sample.
8
Part B: Analysis of Interview Findings
Sources of Supply
Table 2 details the different sources of supply utilized by processors in four years (1997,
1999, 2001 and 2003). Small farms were defined as producers with less than 1 hectare of
land or, for the dairy sector, less than 5 animals. Table 2 presents the number of
enterprises using a particular potential relationship to source farm-level output and the
valid percentage figure corrects for missing data for earlier years in a small number of
cases.
Table 2: Use of potential supply relationships in sourcing agricultural raw materials (1997-2003)
1997
No. Valid %
Spot markets
- all
- with small farmers
- with larger farmers
Contracts
- all
- with small farmers
- with larger farmers
Own farms
Other agents
1999
No. Valid %
2001
No. Valid %
2003
No. Valid %
22
23
10
44.0
44.2
19.6
24
23
15
46.2
44.2
28.3
28
27
16
48.2
45.8
27.6
31
30
15
52.5
50.0
25.4
24
19
22
4
10
46.2
35.8
42.3
7.5
18.5
35
22
34
5
18
66.0
40.7
63.0
9.3
32.7
44
25
42
10
29
74.6
42.4
71.2
17.2
49.2
47
27
45
15
30
78.4
45.0
75.0
25.0
50.0
Table 2 reveals that the use of all potential means for sourcing agricultural raw materials
increased over the period 1997 to 2003. This reflects the impact of macroeconomic
recovery and the overall growth in processor level output during this period and a
requirement to source more raw materials. The greatest growth has been recorded for
contracting with larger farmers (from 42.3 to 75 per cent of the sample), using other
agents and own farms, albeit the latter is from a low base. More enterprises have
contracts with larger farms than with small farms but the reverse is true for sourcing from
spot markets, where relationships with small farms are more prevalent. Between 1999
and 2003, there was relatively little change in the number of enterprises using spot
markets as a source of supply with a slight decline in the number of processors using spot
markets with larger farms in 2003 compared to 2001. These figures would suggest
significant reforms are occurring in farmer – processor relationships: contracting is
becoming more prevalent, especially with larger farmers; the use of spot markets as a
source of supply is stagnating and the use of other agents such as intermediaries and
traders increasing. One quarter of the sample was also engaged in farming in 2003 and
most of this vertical ownership integration has occurred recently: in 1997 only 4
interviewees reported that their enterprise also had farming operations.
9
Contract Assistance Measures
Table 3 details the distribution and mean impact of contract support measures on farm
performance. Measures are listed in descending order of frequency with the most popular
measures applied being prompt payments, transportation and monetary credit. One-third
of the sample also provides physical inputs to at least some of the farms which supply
them. Investment loans from processors to farmers are infrequent. Regarding those firms
that apply a specific measure, the mean percentage of farms which received that measure
in the first year of its operation and the current mean percentage of farms that have access
to the measure is detailed, alongside the percentage of processors that operate a minimum
size policy for applying a particular measure. These figures give an insight into the
diffusion of measures and whether small farms are being excluded. Measures such as
agronomic support, guaranteed prices and prompt payments are typically applied to the
vast majority of farms with which a processor deals. Only 1 processor that offered
prompt payments reported that they discriminated against small farms in applying the
measure. Support measures such as investment loans and the provision of machinery are
more selectively applied – the majority of processors that offer these supports do so
selectively. Around 60 per cent of processors that offer credit and physical inputs also
have a minimum farm size below which they do not offer these supports. Regarding
diffusion, out of the 15 possible support measures listed in Table 3, in only 3 cases is the
mean percentage of farms to which the measure is offered currently lower than in the first
year the measure was introduced by the processor. This suggests that measures tend to be
offered to more farms over time rather than assistance becoming more selective. The
three cases where the mean has fallen are: investment loans, harvesting and handling
support and prompt payments. The first two are capital intensive measures and the fall in
the percentage of farms to which prompt payments are offered is small.
The last three columns of Table 3 assess the mean percentage change in farm level yields,
percentage of output that reaches higher standards and the percentage change in the
amount of output meeting basic standards. The support measures with the largest impact
on yields are the provision of specialist storage, veterinary support and physical inputs,
followed by a set of market measures (prompt payments, guaranteed prices and market
access). Each of these measures is credited with increasing yields by over 10 per cent.
Specialist storage in the form of on-farm cooling tanks has been particularly important in
raising yields and quality in the dairy sector, a trend also noted by Dries and Swinnen
(2002). The impact of investment loans has been modest and this may explain why the
number of farms to which this support is offered has been falling. The provision of
machinery is also credited with having a low impact on farm level yields.
In terms of raising the quality of output, particularly the percentage of output reaching
higher standards, the most beneficial measures have been quality control support,
veterinary support, physical inputs, market access and prompt payments. The link
between quality control, veterinary support and higher quality farm level output appears
reasonable. Machinery, financial and business support, and rather surprisingly,
agronomic support, have had the lowest mean impact. Support measures have had less
10
impact on the amount of farm-level output that reaches basic standards, as most farm
output already passes this threshold. For the latter, the most significant measures have
been quality control, specialist storage and machinery.
Table 3: Distribution and Impact of Contract Support Measures
Distribution of support measure to farms
Measure
Prompt
payments
Transportation
Credit
Physical inputs
Quality control
Guaranteed
prices
Agronomic
Support
Farm loan
guarantees
Machinery
Specialist
storage
Harvest /
handling
Market access
Business / fin.
management
Veterinary
support
Investment
loans
Average
No of
firms
offering
support
measure
% of
sample
offering
support
Mean %
of farms
offered
to in 1st
year
Mean %
of farms
offered
current
28
46.7
88.0
84.5
% of
firms
operate
minimu
m farm
size for
measure
3.7
27
23
20
19
14
45.0
38.3
33.3
31.7
23.3
64.2
39.8
48.2
76.8
86.7
69.6
50.9
51.2
79.4
91.7
13
21.7
82.0
11
18.3
10
9
Impacts of specific contract support
measure on farms
Ave. %
% chge in % chnge
change in
farm
in farm
farm
output
output
yields due reaching
reaching
to
higher
basic
measure
standard
standard
11.4
12.0
2.1
46.2
60.8
57.9
15.8
14.3
6.8
9.3
12.4
7.6
11.1
5.7
8.8
14.2
17.2
8.9
3.5
3.0
3.5
5.6
1.1
84.5
8.3
6.5
5.0
1.4
7.0
15.1
27.3
6.8
6.0
0.0
16.7
15.0
19.4
32.8
30.5
32.9
60.0
33.0
5.0
10.0
4.0
8.3
5.2
4.4
7
11.8
30.6
18.6
71.4
9.3
5.4
2.6
6
6
10.0
10.0
68.3
45.8
69.7
47.5
0.0
50.0
11.2
6.2
14.2
4.2
2.0
2.5
5
8.3
58.0
66.0
40.0
17.0
17.0
0.0
4
6.7
4.0
0.3
75.0
5.5
5.0
2.5
58.2
60.4
35.2
9.1
9.5
2.9
Foreign Investment
Fourteen companies in the sample of 60 have received foreign direct investment (FDI).
Of the 14-FDI firms, 11 have Western foreign investors and in three cases the investment
has come from another FSU state. In comparing against domestically owned firms this
distinction between Western FDI and FSU FDI is maintained. Regarding mean turnover
and employment, there are no significant differences between three groups (Table 4). The
mean turnover per employee, which is often used as a measure of productivity, is higher
for the Western-FDI firms but the difference is not statistically significant. However
significant differences are apparent regarding the amount of capital investment. Over the
11
previous six years, the mean level of capital investment in those enterprises that had
received Western- and FSU-FDI was approximately 1.7 and 2.8 million USD
respectively, compared to a mean of 0.73 million USD for those entirely domestically
owned. A similar significant difference is observed when comparing the amount of
capital investment per employee.
Table 4: Characteristics of Foreign Investment Enterprises
Employment (2003)
Turnover USD (2003)
Turnover per employee
(2003)
Total capital investment
Capital investment per
employee
** Significant at the 5% level
Mean for
Western foreign
investors’ firms
(n=11)
705
6,239,307
Mean for FSUFDI firms (n=3)
F-test
230
1,633,333
Mean for
domestically
owned firms
(n=46)
220
3,087,842
20,000
8353
14,625
1.010
1,706,570
8,431
2,766,667
13,063
726,686
4,648
2.388
1.380
4.712**
3.084**
Marginalization of Small Farmers?
To investigate whether small farms are being excluded from food supply chains, the
survey solicited information on the share of agricultural raw materials procured from
small farms by each processor in four years (1997, 1999, 2001 and 2003). Similar data
was collected regarding the total number of small farms that each processor dealt with in
the same four years. Small farms, as discussed above, were defined as producers with less
than 1 hectare of land or, for the dairy sector, less than 5 animals. From these questions it
is possible to analyze how the share of total agricultural raw materials sourced by
processors from small farms has changed since 1997 together with an assessment of the
number of small farms with which they have a relationship (Table 5). If data was not
available for 1997, the assessment was made on the difference between the least recent
year for which information was available and the figures for 2003. A comparison is also
drawn for the 2001-2003 only, to identify the most recent trends.
12
Table 5: Change in share of agricultural raw materials sourced from small farms and number of
small farms dealt with
Decrease
No change
Increase
Never deal with
small farmers
Total
Change in share of agricultural raw material Change in number of small farms dealt with
sourced from small farms
1997-2003
2001-2003
1997-2003
2001-2003
No.
Percent
No.
Percent
No.
Percent
No.
Percent
22
36.7
18
30.0
13
21.7
11
18.3
12
20.0
20
33.3
3
5.0
8
13.3
15
25.0
9
15.0
33
55.0
28
46.7
11
18.3
13
21.7
11
18.3
13
21.7
60
100.0
60
100.0
60
100.0
60
100.0
For the period 1997-2003, Table 5 indicates that in just over one third of enterprises, the
share of agricultural raw materials sourced from small farms declined with an increase
registered in about a quarter of interviewees’ businesses. Twelve firms report no change
and 11 have never dealt with small farmers. In terms of the number of small farms dealt
with, however, a majority report an increase. This increase in the number of small farms
in many cases was due to political reforms (land reform and decollectivization) rather
than processors’ strategies. For example 10 out 12 companies in Moldova reported an
increase in the number of small farms they dealt with over the period 1997-2003. During
this era, Moldova implemented a radical National Land Program that saw the break up of
former state and collective farms with distribution of land and physical assets to
members.
Only 13 of the enterprises interviewed reported that they dealt with fewer small farms in
2003 than in 1997 and 3 indicated no change over this time period. This implies that there
are a number of processors for which while the share of agricultural raw materials
sourced from small farms is declining are nonetheless dealing with more small farms. For
the 2001-2003 period slightly fewer processors recorded a growth in the number of small
farm suppliers and this may reflect some consolidation. Overall, there is a lack of
evidence of small farms being systematically excluded.
The characteristics of the 13 enterprises that reduced the number of small farms that they
dealt with between 1997 and 2003 are presented in Table 6. Of the 13 enterprises, 6 are
located in Russia, 3 in Georgia, 2 in Armenia and 1 each in Ukraine and Moldova.
Compared to the rest of the sample, those firms which have reduced the number of small
farms that they deal with are larger when measured by the total number of employees but
this is significant only at the 10 per cent level. There are no significant differences
between the two groups regarding their turnover, percentage of sales to the domestic
market and foreign investment. On this measure, there is no evidence of a linkage
between FDI and the exclusion of small farms.
13
Table 6: Comparison of the characteristics of firms that have reduced the number of small farms
that they deal with compared to the rest of the sample
No. of employees (2003)
Turnover (2003)
% of sales to domestic market
% of enterprise shares owned by
domestic private investors
% of enterprise shares owned by
Western foreign investors
* Significant at the 10% level
Mean for firms that
have reduced no. of
small farm
suppliers (n=13)
602
2,566,883
80.4
86.2
Mean for
rest of
sample
(n=47)
6.15
228
3,876,673
71.2
80.2
13.4
t-test
-1.788*
.686
-.813
-.560
.842
Product Quality
For the years 1997, 1999, 2001 and 2003, dairies were asked to indicate the percentage of
milk delivered to them that was extra class, first class, second class and rejected /
unusable. Enterprises without dairy operations were asked, for the same years, to indicate
the percentage of agricultural raw materials supplied to them that was of premium
quality, acceptable quality and rejected / unusable. From these figures it is possible to
assess changes in the quality of farm produce supplied to processors. An improvement
indicates that a greater proportion of produce fell into premium / extra class categories
with less being deemed unusable or rejected.1 Table 7 reveals that the majority of firms
report an improvement in the quality of farm level produce supplied to them. 16 reported
that quality worsened with 7 enterprises indicating no change.
Table 7: Overall change in the quality of farm produce supplied to processors (1997-2003)
Classification category
Worse
No change
Improvement
Total
Frequency
16
7
37
60
Percent
26.7
11.7
61.7
100.0
A breakdown of changes in quality by country (Table 8) reveals that 7 of the 12
companies in the Russian sample indicate that quality decreased over the assessed period.
The 3 companies in Moldova that had suffered from a decrease in product quality were
all engaged in fruit and vegetable processing. In 2003, Moldova suffered from a
particularly cold winter and dry spring and this was seen as the main explanation for
failing yields and quality in these cases (FAO, 2003). Similar reasons were given by the
interviewees for the two cases of worsening product quality in Armenia. In Russia and
Ukraine there is no clear link with a particular sector or factor.
1
The comparison was made for 1997 to 2003. If data for 1997 was not available, the comparison was made
for 1999 with 2003.
14
Table 8: Overall change in the quality of farm produce supplied to processors by country
Armenia
Georgia
Moldova
Russia
Ukraine
Total
Worsen
2
1
3
7
3
16
Change in product quality
No change
Improve
2
8
0
11
0
9
0
5
5
4
7
37
Total
12
12
12
12
12
60
Product Quality and Contracting
It is possible to look at the linkage between the product quality data reported above and
contracting in two ways. First, are there significant differences between the firms that
report improving, no change and worsening product quality and the percentage of
agricultural raw materials procured using contracts? Second, one would expect that an
improvement in product quality is associated with the use of the contract assistance
measures detailed in Table 3. Table 9 reveals that there are significant differences
between firms that report worse, no change and improved product quality on both these
measures. Those firms that report an improvement in the quality of agricultural raw
materials supplied to them procure a greater proportion using contracts. On average those
that have witnessed an improvement in farm level product quality, procure 56.5 per cent
of agricultural raw materials using contracts compared to only 30.3 per cent for those that
have suffered from worsening product quality. A significant difference is also apparent
regarding the mean number of contract assistance measures employed (based on the 15
possible assistance measures listed in Table 3) and product quality. The mean number of
contract assistance measures employed by firms that have witnessed improved product
quality is 4.24 compared against 2.00 and 1.86 for those registering a worsening situation
and no change respectively.
Table 9: Relationship between contracting and product quality
Change in product quality
supplied
Worse
No change
Improve
Total
Percentage of raw material
bought using contracts in 2003
30.3
37.9
56.5
47.4
F-test (ANOVA comparison of
3.014*
means)
*** 1% level of significance, * 10% level of significance
Mean number of contract
support measures used
2.00
1.86
4.24
3.37
6.195***
15
First Mover Advantage and the Impact of Contracting
For each contract support measure employed, information was elicited from interviewees
on their motivation for introducing the measure. Motivations were divided into proactive
reasons (improve product quality, gain a competitive advantage, meet customer needs) or
reactive (defend supply base against competition, survival etc.). While not all
explanations could be divided into proactive or reactive reasons, in about 190 cases
motivations could be categorized in this manner. The mean impact of contract support
measures when employed for proactive or reactive reasons is compared in Table 10.
Table 10: Mean impact of contract support measures introduced for proactive or reactive reasons
Mean % change in yields
Mean % change in output meeting highest grade
Mean % change in output reaching basic standards
Motivation
Reactive
Proactive
No.
44
150
Mean
5.07
10.33
Std. Dev.
8.98
11.45
Reactive
Proactive
43
150
3.17
11.35
8.41
13.29
Reactive
Proactive
43
148
1.67
3.22
4.83
6.03
t-test
-2.803***
-3.812***
-1.546
*** significant at 0.01 level
Table 10 reveals that the mean impact on yields and the percentage of output reaching
highest grade standards was significantly larger for measures introduced for proactive
reasons. For the percentage of output reaching basic standards, where impacts overall are
more modest, no significant differences are evident. For the other two measures the
differences between the proactive and reactive groups are striking: the mean impact on
yields is twice as great for the proactive group and the differential for the percentage
change in output reaching highest grade standards is even higher. This suggests that the
impact of contracting is not uniform but depends on the nature of the market and the
degree to which other firms also offer contract assistance measures. First movers in
offering contract assistance measures appear to reap the greatest rewards. Where
contracting is introduced to protect the supply base of a processor which is under threat
from others, contracting still has a positive effect on yields and quality but the benefits
are far smaller.
While there are significant differences between the impact of contract support measures
introduced for proactive and reactive reasons, there are no significant relationships with
year of introduction (Table 11). The survey recorded the year in which each contract
support measure was introduced and they have been coded into four categories: up to and
including 1996, 1997 to 1999, 2000 to 2001 and 2002 to 2003. The mean impact on
yields and percentage change in farm output reaching the highest standards was highest
for measures introduced in the period 2000 to 2001, but differences between year groups
are not significant. This would suggest the impact of contracting is related more to
16
market conditions such as the contract relationships of competitors rather than the
specific year of introduction.
17
Table 11: Comparison of Impact of Contract Support Measures by Year Introduced
Year groups
1996 and previous Mean
No.
Std. Dev.
1997-1999
Mean
No.
Std. Dev.
2000-2001
Mean
No.
Std. Dev.
2002-2003
Mean
No.
Std. Dev.
Total
Mean
No.
Std. Dev.
Mean % change Mean % change in Mean % change in
in yields
output meeting output reaching basic
highest grade
standards
6.88
9.96
3.09
34
34
34
8.34
9.06
6.52
9.24
9.92
3.55
77
77
76
11.65
11.91
6.40
11.18
10.81
2.44
50
49
48
12.61
16.36
5.73
8.36
6.31
1.78
32
32
32
9.89
12.31
3.10
9.18
9.55
2.89
193
192
190
11.14
12.84
5.82
Anova F-test
1.078
0.866
0.824
FDI, Product Quality and Contracting
Comparing firms which have received FSU or Western FDI with the rest of the sample
regarding contracting and product quality, significant differences are apparent (Table 12).
First, Western FDI enterprises use a significantly greater number of contract support
measures (as listed in Table 3) than the other 2 groups. Western FDI firms use an average
of 4.91 contract support measures compared to means of 1.67 and 3.12 for FSU-FDI
firms and wholly domestically owned companies respectively. Second, and somewhat
surprisingly, both Western and FSU FDI firms source a significantly greater proportion
of agricultural raw materials from small farms. Enterprises with Western and FSU
foreign investment source on average 57.3 and 63.3 per cent of their agricultural raw
materials from small farms (both spot markets and contracts) respectively compared to
30.9 per cent for domestically owned companies. Domestically owned companies source
a significantly greater proportion from other agents (30.2 per cent of total supplies)
compared to a mean of 5.7 per cent for Western FDI-firms (Table 12). Both Western and
FSU FDI-firms are also engaged in far greater levels of exporting. While about four-fifths
of the output of wholly domestically owned firms is sold on the domestic market, the
respective figures for Western and FSU FDI-firms are only 47.9 and 55.7 per cent.
18
Table 12: Characteristics of Foreign Investment Enterprises regarding contracting
Mean for
Western foreign
investors’ firms
(n=11)
Mean for
FSU-FDI
firms (n=3)
Mean for
domestically
owned firms
(n=46)
F-test
between three
groups
Total number of
4.91
1.67
3.12
2.857*
contract support
measures used
% of supplies
73.3
3.66
44.0
5.256***
bought using
contracts (2003)
**% of supplies
17.5
66.7
21.8
2.8836*
bought from spot
markets (2003)
% of supply from
57.3
63.3
30.9
3.685**
small farms (both
spot markets and
contract) 2003
% of supply from
33.6
7.0
34.9
1.206
large farms (both
spot markets and
contract) 2003
% of supply
5.7
11.3
30.2
2.852*
bought from other
agents
% of output sold
47.9
55.7
80.2
4.272**
on domestic
market
* Significant at the 10% level; ** Significant at the 5% level, *** significant at the 1% level
T-test
comparing
Western FDI
and domestic
owned only
-2.059**
-2.382**
0.402
-2.417**
0.125
2.244**
2.809***
Exporting and Relationships with Investment and Contracting
From the 60 enterprises in the dataset, 32 are engaged in exporting (53.3 per cent). About
one quarter of the sample export more than 50 per cent of their total output and as
detailed in Table 12, both Western FDI and FSU-FDI firms export a significantly greater
proportion of their output. Examining other associations, Table 13 presents a correlation
coefficient matrix detailing the relationships between the percentage of sales to the
domestic market and the key variables on contracting, use of contract support measures
and investment.
The correlations between the percentage of total sales accounted for by the domestic
market and other variables are marked in bold font. There are significant, negative
correlations between the percentage of sales to the domestic market and total investment,
capital investment per employee, the proportion of total supply from small farms and the
total number of contract support measures. In other words exporting is associated with
greater capital investment and exporters source proportionally more from small farms and
employ a higher number of contract support measures. Given the linkage between
19
exporting and FDI, the similarity between the supply relationships with exporting (Table
13) and the supply characteristics of FDI-firms (Table 12) is understandable.
20
Table 13: Correlation Coefficient Matrix for Relationships between Exporting, Contracting and Investment
Value of total
investments
Value of total investments
% of supply from small
farms (both spot markets
and contract) 2003
Total number of contract
support measures used
% of sales to domestic
market
% of ag. supply from other
agents (2003)
% of raw material bought
using contracts (2003)
% of supply Total number
from small
of contract
farms (both
support
spot markets & measures used
contract) 2003
1
.232*
.210
1
% of raw
Capital
% of sales to % of ag. supply
from other material bought investment per
domestic
employee
market agents (2003) using contracts
(2003)
(2003)
Total
employment
(2003)
-.313**
-.298**
-.015
.563***
.151
-.023
-.353***
-.576***
.096
.373***
-.128
1
-.322**
-.303**
.393***
.042
.104
1
.295**
.048
-.232*
.227*
1
-.512***
-.278**
-.063
1
.188
-.084
1
-.153
Capital investment per
employee (2003)
Total employment (2003)
1
*** Correlation is significant at the 0.01 level; ** Correlation is significant at the 0.05 level; * Correlation is significant at the 10% level
21
Future Developments
As part of the survey, interviewees were asked how and why the number of suppliers to
their enterprise would change in future. From these responses, predicted changes in the
number of suppliers were categorized as likely to decrease, no change or increase (Table
14).
Table 14: Predicted change in number of suppliers
Predicted likely change
Frequency
Valid Percent
Decrease
19
32.2
No change
16
27.1
Increase
24
40.7
Total1
59
100.0
1
One interviewee did not feel able to make a prediction
The largest single category (40.7 per cent) predict that their enterprise will increase its
number of suppliers in future and the main reason for this is an expected expansion in
demand. While just under one-third predict a decrease in the number of suppliers, this
does not imply that the majority within this group have a policy of deliberately excluding
certain farms. Out of the 19 cases, 12 cite principally farmer led reasons for their
predictions of a future fall in the number of suppliers. The two main farmer led reasons
cited were a movement into different agricultural activities (for example milk farmers
slaughtering their dairy cows because of high current meat prices) and an exit of small
farmers as a result of an upturn in fortunes in the rest of the economy which will make
the low returns to small-scale agriculture less attractive. The latter pattern has been
witnessed in many Central European states and should not be interpreted as a deliberate
marginalization of small farms. Only 7 interviewees cited that they have specific
initiatives for reducing the number of farmers they deal with and in 3 cases this was
linked to backward integration into farming.
While noting the small size of sub-samples, comparisons can be drawn between current
supply practices and predicted future changes in suppliers (Table 15). There are no
significant differences in the size of enterprises predicting increases, decreases or no
change in the number of suppliers. However, those that are predicting a future rise in the
number of suppliers, currently deal with a small number of large farms (mean of 19
compared to averages of 113 and 223 for the decrease and no change groups
respectively). It may be that they are operating in regions with a dearth of large farms and
thus to meet rises in demand they require an increasing number of suppliers. The firms
predicting a future growth in the number of suppliers also deal with substantial numbers
of small farms (mean of 883), although differences between the three groups are not
statistically significant.
There are no significant differences between the predicted increase, decrease and no
change groups in terms of the amount supplied via spot markets or contracts with large
farms. However, regarding contracts with small farms there are significant differences
with those predicting a fall in the number of suppliers sourcing a high proportion of total
22
supply from small farms via contracts (31.8 per cent compared to just 1.9 per cent of the
no change group). This may suggest that small farms could be squeezed in future
although other questions have indicated that the fall in small farms is more likely to be
farmer-led than as a result of specific plans to exclude them by processors.
Table 15: Comparison between current supply practices and predicted future changes in number of
suppliers
Practices / figures for 2003
Means for
predict decrease
group
171
31.8
Means for no
change group
Total employment
315
Share of supply from contracts
1.9
with small farms
Share of supply from contracts
28.4
36.3
with large farms
Share of supply from spot markets
19.9
13.0
with small farms
Share of supply from spot markets
7.7
5.1
with large farms
Share of supply from other agents
8.2
39.3
Number of small farm suppliers
351.4
600.9
Number of large farms suppliers
112.5
222.9
*** Significant at the 0.01 level; ** Significant at the 0.05 level;
Means for
increase group
F-test
424
19.6
0.712
5.288***
23.5
0.932
20.8
0.325
1.3
1.534
28.9
883.0
19.0
4.750**
0.535
2.292
Qualitative Findings on Business Constraints and National Government and World
Bank Priorities
In addition to collecting numerical data, respondents were asked several open-ended
questions: to identify the key constraints that their business faced, the impact of national
government decision-making on their enterprise, and the role they saw for international
agencies such as the World Bank. These questions generated a rich and diverse set of
answers some of which were specific to particular states and markets. This section
summarizes the most widespread opinions on these issues.
Regarding business constraints, the most commonly cited problem was a lack of effective
market governance. This has two main elements: first, the continued operations of
‘shadow / black’ economy producers that avoid taxes, social security obligations and
have engaged in counterfeiting brands and smuggling. Such producers, by avoiding these
obligations, have a lower cost structure and are able to undercut other producers. For
example, one cheese manufacturer in Armenia suggested that differences in tax payments
accounted for a 25 per cent variation in producers’ costs and a winemaker in Moldova
indicated that excise duties on alcohol were equal to 50 per cent of the cost of raw
materials. Counterfeiting has undermined the value of nationally well known brands,
particularly in the wine and spirits sector. One Georgian wine producer indicated that
counterfeiters were falsifying one of their premium brands and were selling it at a price
lower than which the company was paying for its raw materials. Clearly, from a
23
marketing perspective it is difficult to develop premium brands and added value products
in a business environment characterized by weak governance and legal protection.
Second, demands for bribes by inspection agencies and state officials were reported as
still being widespread. For example one Moldovan fruit and vegetable processor reported
that about twenty different state controls were introduced in their industry on a yearly
basis with little benefit to consumers or producers, only those administrating them. This
point was echoed by most interviewees in Georgia. The culture of national state
administrations was seen as a major impediment to the effective implementation of loans
and aid from international agencies.
The most frequently cited resource issue was problems procuring raw materials. As final
demand in the region has increased, and in some cases farm production has been severely
disrupted by land reform, the procurement of supplies of sufficient quantity and quality
has become more challenging and this has motivated the increased diversity of sources
(Table 2) and the investment in contracting and contract support measures (Table 3). This
has been particularly noticeable in the dairy sector but not limited to it; respondents from
wineries and the fruit and vegetable sector reported similar difficulties. While the survey
did not explicitly collect data on prices, real increases in both raw material and final
product prices were reported by many.
Internationalization is perceived as the main marketing challenge, both in terms of coping
with growing imports and also effectively serving a wider geographical market either at
the national or international level. The main barriers to exporting are perceived as
harmonizing production with international standards, establishing effective distribution
and poor bargaining terms as a result of the strong negotiating positions of intermediaries
(in particular supermarkets). Distributors often default on exclusivity agreements,
carrying competitors’ lines, despite the existence of contracts which prohibit such
actions. Helping meet the challenge of the internationalization of markets was identified
as an investment / policy priority. As identified in Table 12, export experience is largely
confined, with a few notable exceptions, to FDI-firms. For wholly domestically owned
firms, international assistance to aid the harmonization of national and international
standards and provide technical advice on exporting was seen as particularly important
as, as one respondent remarked, such specialist advice typically cannot be obtained from
local universities, colleges and other educational agencies.
To conclude, the most widespread problems faced by processors are ineffective or
inappropriate market governance, problems procuring agricultural raw materials and
meeting the challenge of the greater internationalization of markets. These challenges are
common across the states surveyed and should form the basis of any policy initiatives.
Conclusions and Policy Recommendations
Overall, the spread of contracting has been beneficial.
Based on survey findings, farm-processor contracting has become more prevalent in the
FSU region, contract support measures have stimulated improvement in yields and the
24
quality of output, and such supports have been introduced in the majority of cases for
proactive reasons. There is a significant association between improvements in product
quality and the percentage of output bought using contracts and the mean number of
contract support measures employed.
Improving yields and output
Investment loans and machinery grants have been mainstays of many development
projects yet the impact on yields and improvements in product quality of these two
measures in the FSU has been modest. Specialist storage (especially cooling tanks in the
dairy sector), veterinary support, prompt payments, guaranteed prices and physical inputs
have had bigger effects on average yields. Improving the proportion of output reaching
higher standards has been achieved most successfully through quality control, market
access, veterinary support and physical inputs. This suggests that improvements to yields
and quality are linked to five main factors:
a) Preserving the quality of what is already produced. A major problem in the FSU
has been the storage of production prior to processing. In the dairy sector the lack
of effective cooling facilities rapidly decreases the value of milk produced and in
the arable sector, post-harvest losses through inappropriate storage have eroded
competitiveness (Striewe, 1999). Investments in farm level production will
generate poor returns if the effective means to store output prior to processing are
absent.
b) The impact of veterinary support on yields and product quality has also been
significant. While returns to agronomic support have been modest, interviewees
repeatedly stressed that a willingness to learn, take on board advice and a
professional attitude was as, if not, more important than size in establishing a
fruitful farm-processor relationship. Land reform programs have created a more
diverse and fragmented agricultural base in most FSU states. Disseminating
technical advice to farms becomes more difficult under these conditions and
attention needs to be given to how this can be best achieved. While farmprocessor contracting is one mechanism for the dissemination of technical advice,
with processors rarely discriminating against small farms on such measures, a
question remains as to whether such private support mechanisms are sufficient in
all areas.
c) Premiums are an important element in stimulating improvements in quality at the
farm level. This underpins why market access, as a potential contract support
measure, is linked to above average improvements in yields and quality. The
availability of financial premiums for higher quality is linked to both final
demand on the domestic market and export opportunities. While poverty is still
endemic in the FSU, a new middle class is emerging and there is an important
opportunity for domestic firms to meet the demand for value-added products both
nationally and internationally.
25
d) The provision of physical inputs has had an above average (compared to other
contract support measures) impact on yields and quality. The mean impact of the
provision of physical inputs has been greater than credit. This may reflect that
credit can be more easily diverted to other, non-farm activities and difficult to
monitor (Gow et al. 2000). Both public and private sector support in the region
has suffered from resources being diverted from the intended programs,
particularly where the use of resources has been difficult to monitor. Programs
that improve market access and the dissemination of veterinary and quality
control advice are likely to have beneficial effects on yields and quality, and offer
an additional advantage in that they should be easier to monitor and thus less
likely to suffer from diversion of resources. Given interviewees’ discussion of
inappropriate market governance, evaluating support measures in terms of the
ease with which resources can be diverted to alternative uses should be one
criterion used in assessing any future policy choices.
e) Improvements in yields and quality are also linked to a set of market measures
particularly prompt payments and guaranteed prices. Cash flow is a major concern
and the linkage between delayed payments and falls in output has been discussed
elsewhere (Gow and Swinnen, 2001).
The positive benefits of FDI are apparent and FDI should be encouraged.
While not significantly different in terms of their size, both Western and FSU-FDI firms
have made significantly greater capital investments, particularly in upgrading processing
faculties. Upgrading processing facilities, particularly so that they can access export
markets, has been identified as a major challenge for the FSU successor states. Western
FDI-firms offer significantly more contract support measures which is linked to
improving yields and increasing farm-level quality. One often expressed concern of FDI
is that it can lead to the marginalization of small farms (Escobal et al. 2000). To date
there is no evidence of this – only one FDI-firm has reduced the number of small farms it
deals with and results presented indicate that FDI-firms actually source a significantly
greater proportion of supplies from small farms.
In dealing with the debate on the marginalization of small farms two sets of distinctions
should be noted. First, marginalization can be defined in terms of (a) an exclusion of
small farms from formal food supply chains and (b) small farms being offered
significantly worse terms and conditions. There is little evidence that small farms are
being excluded but they may receive poorer terms and access to contract support
measures (for example around 60 per cent of processors that offer credit and physical
inputs to farmers do have a minimum farm size below which this contract support
measure is not offered). However, contract support measures have overall become
available to an increasing number of farmers after their introduction rather than support
becoming progressively more selective. There is thus little indication that the introduction
of contract support measures reduces farm access to inputs and technical advice.
Secondly regarding marginalization, a decrease in the number of small farms a processor
deals with can come from either farm or processor level initiatives. To date, farm level
26
initiatives such as switching to different agricultural activities or exiting small-scale
agriculture altogether appear more important than processor led strategies. As economies
recover in the FSU, the exit of labor from small scale agriculture is inevitable and should
not be taken as an indicator of processor led exclusion.
The qualitative data collected indicates that the most widespread problems faced by
processors are ineffective or inappropriate market governance, problems procuring
agricultural raw materials and meeting the challenge of the greater internationalization of
markets. Helping meet the challenge of internationalization was identified as an
investment and policy priority. It is noticeable that exporting is, with a few notable
exceptions, largely limited to FDI-firms and most domestically owned firms lack
experience in this field. International assistance to aid the harmonization of national and
international standards and provide technical advice on exporting was seen particularly as
important given that such specialist advice typically cannot be obtained from local
educational establishments.
References
Blanchard, O. and Kremer, M. (1997), ‘Disorganization’, Quarterly Journal of
Economics, Vol.112, No.4, pp.1091-1126
Churchill, G. A. (1999), Marketing Research: Methodological foundations, Orlando:
Dryden Press.
Dries, L. and Swinnen, J.F.M. (2002), Finance, Investments, and Restructuring in Polish
Agriculture, Research Group on Food Policy, Transition & Development, Katholieke
Universiteit Leuven, mimeo
Escobal, J., Agreda, V. and Reardon, T. (2000), ‘Endogenous institutional innovation and
agroindustrialization on the Peruvian coast’, Agricultural Economics, Vol.23, pp.267277
FAO, (2003), Moldova: Tight food supply envisaged following a severely cold winter
and exceptionally dry spring, FAO Global Information and Early Warning System on
Agriculture and Food, 22nd July,
http://www.fao.org/docrep/005/y9985e/y9985e00.htm
Gow, H.R., Streeter, D.H. and Swinnen, J.F.M. (2000), ‘How private contract
enforcement mechanisms can succeed where public institutions fail: the case of
Juhocukor a.s.’, Agricultural Economics, Vol.23, No.3, pp.253-265
27
Gow, H.R. and Swinnen, J.F.M. (2001), ‘Private enforcement capital and contract
enforcement in transitional economies’, American Journal of Agricultural Economics,
Vol.83, No.3, pp.686-690.
Lincoln, Y. S. and Guba, E. G. (1985), Naturalistic Inquiry, Beverly Hills: Sage.
Merriam, S. (1998), Qualitative Research and Case Study Applications in Education, San
Francisco: Jossey-Bass.
Striewe, L., (1999), Grain and Oilseed Marketing in Ukraine, Iowa State University
Ukraine Agricultural Policy Project (UAPP), Kiev.
28
Appendix 1: Country Comparisons
Table 16 summaries cross-country comparisons for mean values and ANOVA F-tests
relating to market structure, firm characteristics, supply base and contracting.
Table 16: Comparison of Market Structure, Firm Characteristics, Supply Base and Contracting by
Country
Armenia
Georgia
Moldova
Russia
Ukraine
F-test
Market Structure
% of sales to domestic
63.92
53.86
51.58
96.25
99.83
6.827***
market
of sales to other CEECs
27.50
29.88
39.42
1.50
0.10
5.709***
/ FSU
% of sales to EU-15
0.42
6.82
6.83
1.25
0.00
1.831
% of sales to RoW
8.17
9.44
3.00
1.00
0.07
1.870
Firm Characteristics
Employment (2003)
Turnover (2003)
Value of investments
(USD)
Capital investment
(USD) per employee
(2003)
% of cap. privately
owned
% of cap. foreign owned
Supply Base
No. of suppliers (2003)
No. of suppliers (2001)
No. of suppliers per
employee (2003)
No. of suppliers per
employee (2001)
133.92
3,305,603
607,250
526.58
1,460,057
651,908
259.00
3,678,057
2,009,909
218.08
1,808,042
409,667
408.75
7,712,667
1,520,500
8,064
3,290
9,878
3,610
3,978
2.245*
80.58
70.26
61.88
94.92
100.00
3.188**
19.42
21.41
37.54
0.00
0.00
673.0
534.5
4.6
388.6
252.8
6.2
728.7
550.7
2.9
184.7
155.8
8.3
1,650.9
879.3
10.5
1.261
0.675
0.301
6.0
5.7
3.3
6.7
1.5
0.327
Contracting
% of raw material
59.25
32.42
64.96
46.42
33.75
bought using farm
contracts (2003)
Mean number of
4.00
3.67
5.33
1.50
2.33
contract support
measures employed
% of supply from small
42.67
40.58
55.67
25.89
22.08
farms (both spot &
cont.)
% of supply from large
33.25
30.58
37.46
26.61
38.33
farms (both spot &
cont.)
Number of small farms
605.64
338.17
639.64
165.00
1,409.09
dealt with (2003)
Number of large farms
186.42
17.58
79.92
14.67
216.00
deal with (2003)
* Significant at the 10% level; ** Significant at the 5% level, *** significant at the 1% level
0.623
2.184*
2.861**
3.667***
1.834
4.758***
1.919
0.296
0.982
1.195
29
From Table 16, the discussion presented here is limited to those findings which are most
noteworthy. The percentage of sales accounted for by the domestic market varies
significantly between countries. In Russia and Ukraine most sales are made within
national boundaries, while for Armenia, Georgia and Moldova a greater proportion of
sales are exported. The greater proportion of exports for Armenia, Georgia and Moldova
reflects the smaller size of their domestic market and limited national opportunities for
growth. On closer examination it is revealed that the majority of exports go to other
CEECs / FSU. No statistically significant differences are recorded for export sales to the
EU-15 or the rest of the world.
Whilst differences exist between countries in terms of firm size as measured by the mean
number of employees (the mean figure for Armenia being nearly four times that for
Georgia), these differences were not found to be statistically significant. By contrast,
differences in mean turnover between countries were statistically significant at the ten per
cent level, with Georgian and Russian firms reporting the lowest turnovers, and Ukraine
the highest. Given the number of comments made relating to high taxation rates and the
shadow economy, one must be careful in interpreting turnover data - it is not always clear
whether data reflect genuine differences in terms of the performance of firms, or rather a
difference in terms of willingness to divulge accurate financial data.
The mean values of capital investments are significantly higher in Moldova and Ukraine,
than for Armenia, Georgia and Russia. When analyzing the amount of capital investment
per employee the highest figures are recorded for Moldova and Armenia. Capital
investment in both absolute terms and relative to firm size has been modest in the
Georgian and Russian samples. The Moldovan results reflect the higher level of foreign
direct investment which is linked significantly with capital investment (see Table 4).
No statistically significant differences were recorded between countries in relation to
firms’ supply bases. Whilst recognizing this, it is interesting to note the extent to which
supplier numbers vary from country to country. For example, Ukrainian firms utilized
nearly ten times as many suppliers as their Russian counterparts in 2003. In part this
reflects differences in firm size, differences are far less apparent when considering the
number of suppliers per employee. The number of suppliers per employee can be taken as
indication of the degree of fragmentation of the supply base. The lowest supplieremployee ratio was recorded for Moldova, which along with Armenia was the only
country to see a reduction in the supplier-employee ratio between 2001 and 2003. The
fall in Moldova may reflect a degree of consolidation in land management following land
reform and radical decollectivization in the late 1990s
Only one statistically significant relationship was identified for contracting, namely the
mean number of contract support measures employed. The number of support measures
offered is significantly higher in Armenia, Georgia and Moldova than Russia and
Ukraine. This may reflect the greater level of FDI in the Armenian, Georgian and
Moldovan samples, given the previously found link between FDI and contract support
(Table 12).
30
Table 17 details the use of contract support measures by country. Contract support
appears to be most well developed in Moldova and least prevalent in Russia and Ukraine.
The figure for Moldova may reflect: the importance of export markets, the importance of
wine / brandy, fruit and vegetable and sugar production (see Appendix 2) and the
influence of FDI. Credit is widely given in Moldova and a majority also provides prompt
payments and transportation. In Russia, by contrast, apart from transportation and
machinery, other support measures are offered by processors in 25 per cent or less cases.
It is noticeable that not a single processor in Russia reports that they offer prompt
payments or guaranteed prices. The Russian sample is overwhelmingly domestically
owned and it may be that if FDI occurred and foreign investors started providing better
farm level support, domestically owned processors would be forced to improve their
offerings.
Table 17: Percentage of firms in each country offering particular support measure to at least some of
the farms they deal with
Armenia
No of firms
Measures
Credit
Physical inputs
Machinery
Transportation
Agronomic support
Veterinary support
Bus / financial man. support
Harvest / handling support
Farm loan guarantees
Investment loans
Specialist storage
Quality control
Market access
Prompt payments
Guaranteed prices
Mean number of contract
support measures
Georgia
Moldova
Russia
Ukraine
12
12
12
12
12
41.7
50.0
0.0
16.7
16.7
25.0
8.3
0.0
25.0
0.0
8.3
50.0
33.3
75.0
50.0
4.00
8.3
16.7
8.3
83.3
33.3
0.0
0.0
25.0
0.0
8.3
41.7
33.3
8.3
75.0
25.0
3.67
75.0
41.7
25.0
58.3
41.7
16.7
41.7
16.7
41.7
25.0
16.7
33.3
8.3
66.7
25.0
5.33
25.0
25.0
41.7
50.0
0.0
0.0
0.0
0.0
8.3
0.0
0.0
0.0
0.0
0.0
0.0
1.50
41.7
33.3
8.3
16.7
16.7
0.0
0.0
16.7
16.7
0.0
8.3
41.7
0.0
16.7
16.7
2.33
31
Appendix 2: Sector Comparisons
The main characteristics of firms in each sector and their contracting and supply
relationships are summarised in Table 18.
Table 18: Comparison of Means for Market Structure, Firm Characteristics, Supply Base and
Contracting by Sector
Liquid
Speciality
Wine /
Fruit and
Sugar
Other
milk
dairy
brandy
Veg.
No. of companies
20
10
10
8
4
8
Market Structure
% of sales to dom. markt
% of sales to other
CEECs / FSU
% of sales to EU-15
% of sales to RoW
Firm Characteristics
Employment (2003)
Turnover (2003)
Value of investments
(USD)
Capital invest (USD) per
employee (2003)
% of capital privately
owned
% of capital foreign
owned
Supply Base
No. of suppliers (2003)
No. of suppliers (2001)
No. of suppliers per
employee (2003)
No. of suppliers per
employee (2001)
Contracting
% of raw material bought
using farm contracts
(2003)
Total number of contract
support measures
employed
% of supply from small
farms (both spot & cont.)
% of supply from large
farms (both spot & cont.)
Number of small farms
deal with (2003)
Number of large farms
deal with (2003)
Total
60
96.8
2.1
78.5
16.0
24.0
62.3
46.2
35.3
95.0
5.0
84.4
6.8
73.1
19.7
0.8
0.4
0.0
5.5
2.6
11.1
12.2
7.6
0.0
0.0
5.6
3.3
3.1
4.3
283.9
3,739,089
530,167
94.1
1,914,178
446,333
245.2
5,867,101
1,831,119
193.5
2,498,179
1,322,125
462.5
8,022,500
2,850,000
760.9
1,362,887
436,463
309.3
3,592,885
1,005,963
2743.6
9221.2
7231.2
10,303.7
6140.4
2040.3
5754.4
85.9
91.5
67.7
70.6
74.4
90.0
81.5
11.0
8.0
32.3
16.9
25.6
10.0
15.7
689.9
185.5
6.28
75.0
61.7
2.90
1638.8
1196.4
11.24
484.8
399.0
13.64
1979.3
2298.5
3.52
54.1
47.0
0.47
715.5
457.7
6.55
1.45
3.33
12.76
11.71
3.37
0.57
4.82
58.8
38.5
47.8
51.3
52.4
22.9
47.4
3.25
3.50
4.20
3.63
5.75
1.00
3.37
33.8
28.7
79.1
41.4
6.3
16.7
37.4
39.7
19.8
12.0
57.4
61.1
22.5
33.2
635
30
1604
382
1275
39
618
88
39
30
103
704
10
103
32
Comparing across the 6 sectors, liquid milk, sugar and the ‘other category’ are most
oriented to the domestic market. Exporting is significant in the wine / brandy and fruit
and vegetable sectors, where in both cases most exports go to other CEECs and FSU
states. Little is exported to the EU-15 or the rest of the world. Developing export markets
outside of the FSU remains a major challenge.
The largest companies by employment are in the sugar and ‘other’ industries. By
turnover, sugar, wine / brandy and liquid milk processing have the largest mean sizes.
The highest mean capital investments have been recorded in the sugar, wine / brandy and
fruit and vegetable sectors. In each sub-sector, the majority of firms are privately owned
but foreign investment has been highest in wine / brandy and sugar processing.
There are significant differences between sectors in terms of their supply bases. Table 18
records the mean number of suppliers (sum of farms dealt with via spot markets and
contracts and other agents / distributors). In most sectors, processors deal with a large
number of suppliers: the means for the sugar and wine / brandy sectors in 2003 were
1979 and 1639 respectively. Comparing the number of suppliers in 2001 and 2003, in all
sectors except sugar, the total number of suppliers increased between the two dates. In the
sugar industry, the mean number of suppliers fell from 2,299 in 2001 to 1,979 in 2003,
although one should remember that only 4 companies are included in this sector. The
mean number of suppliers per employee in the processing plant gives an indication of the
degree of fragmentation of the supply base. The highest fragmentation is recorded in the
fruit and vegetable sector with, in 2003, a mean of 13.6 suppliers per employee in
processing. This may reflect how fruit and vegetable production has low entry barriers.
The most concentrated supply bases using this measure are recorded in the speciality
dairy (cheese, ice cream, kefir etc.) and sugar sectors.
Analysing the use of contracts and the balance between small and large farm suppliers it
is evident that a greater proportion of raw materials are bought using contracts with
farmers in the liquid milk, fruit and vegetables and sugar sectors. In the speciality dairy
sector the use of other agents and wholesalers is more prominent and this probably
reflects a degree of specialisation in farm relationships (see Appendix 3). A noticeably
low proportion of supply comes from small farms in the sugar sector (6.3 per cent)
although sugar refineries do deal with a large number of small farms (1,275 in 2003). In
the wine / brandy sector over three-quarters of grapes come from small farms and this
may reflect why so many wineries in the FSU wish to purchase vineyards to provide a
more stable supply of grapes that meets their quality requirements. In the other category,
the use of other agents / distributors is also significant and in this sub-sector the use of
contract support measures is limited.
Contract support measures are most widely used in the sugar sector (mean of 5.75
measures employed per processor) and for wine / brandy. This may reflect how (a)
processors are procuring directly from farmers rather than agents / distributors, (b) FDI
has been more significant in these sectors and (b) quality requirements are more acute in
these sectors.
33
Table 19 provides a more detailed guide to the use of particular support measures in each
sector. While noting the small number of responses, credit, agronomic support, harvest /
handling support and farm loan guarantees are offered by 3 out of the 4 sugar refineries.
All four of the sugar processors offer physical inputs to at least some of the farmers they
deal with. Most wineries offer transportation and agronomic support. Veterinary support
is less prevalent than agronomic support and investment loans are not common in any
industry. Prompt payments appear to be more common in the wine / brandy, fruit and
vegetables and speciality dairy sectors. In these cases many deals are based on cash
transactions. Two out of the four sugar refineries offer guaranteed prices; in contrast to
the liquid milk sector where only 2 out of the 20 dairies offer such support.
Table 19: Percentage of firms in each sector offering particular support measure to at least some of
the farms they deal with
Liquid milk
No of firms
Measures
Credit
Physical inputs
Machinery
Transportation
Agronomic support
Veterinary support
Bus. / financial
man. support
Harvest / handling
support
Farm loan
guarantees
Investment loans
Specialist storage
Quality control
Market access
Prompt payments
Guaranteed prices
Mean number of
contract support
measures
20
Speciality
dairy
10
Wine /
brandy
10
50.0
30.0
35.0
55.0
0.0
15.0
15.0
50.0
20.0
0.0
40.0
10.0
20.0
10.0
0.0
Fruit and
Veg.
Sugar
Other
8
4
8
30.0
50.0
10.0
60.0
60.0
0.0
0.0
25.0
37.5
25.0
37.5
37.5
0.0
12.5
75.0
100.0
0.0
25.0
75.0
0.0
25.0
0.0
0.0
0.0
25.0
0.0
0.0
0.0
0.0
30.0
12.5
75.0
0.0
10.0
20.0
20.0
25.0
75.0
0.0
10.0
15.0
45.0
5.0
30.0
10.0
10.0
20.0
30.0
20.0
60.0
40.0
0.0
10.0
50.0
10.0
70.0
20.0
12.5
12.5
12.5
12.5
63.0
37.5
0.0
25.0
0.0
0.0
50.0
50.0
0.0
12.5
12.5
12.5
25.0
12.5
3.25
3.50
4.20
3.63
5.75
1.00
34
Appendix 3: Dairy Industry
The largest sub-sector in the sample is firms that have some involvement in dairy
processing (30 companies). This group is, however, quite diverse with 2 distinct product
groups: liquid milk pasteurizers and second, specialty dairies for which value-added ice
cream, dairy and / or cheese products are their most important lines. In a few cases dairy
processing is not the main activity of the enterprise but their activities in monitoring milk
quality were noted (Section 4 of the questionnaire), with the questions set drawing on
previous survey work by Dries and Swinnen (2002). Considering procedures for testing
milk quality and adjusting payments accordingly (Table 20) all processors test for fat
content and the overwhelming majority also assess consistency, residiums and germ
content. While all but two dairies modify payments based on fat content, adjustments for
other dimensions of quality such as cell, protein and germ content are less common.
Table 20: Testing and adjustment of payments for milk
a) Fat content
b) Cell content
c) Germ content
d) Milk consistency
e) Dry defatted residium
f) Protein content
Number of
% of dairies test Number of dairies
dairies test milk milk on purchase adjust payments
on purchase
based on level
30
100.0
28
20
66.7
7
24
80.0
11
28
93.3
14
24
80.0
8
19
63.3
9
% of dairies adjust
payments based on level
93.3
23.3
36.7
46.7
26.7
30.0
Broadly speaking, dairies are procuring milk in two main ways depending on the nature
of their operations (Table 21). Plants that principally pasteurize liquid milk have
contracts with large and small farmers as their core supply line and source additional
supplies through spot markets and from other agents (wholesalers, intermediaries). Dairy
plants that have much smaller volumes, such as niche ice cream and cheese producers use
wholesalers, other dairies and intermediaries as their most important source of liquid milk
(mean of 46.6 per cent) and some no longer have direct connections with farmers (Table
21).
Table 21: Differences in sourcing of milk depending on type of dairy
Total percentage of raw material bought
using contracts in 2003
Share of total raw materials from other
agents 2003
Type of dairy
liquid milk
Number
20
Mean
58.80
Std. Deviation
36.3
ice cream, cheese
and specialist
20
38.50
43.6
liquid milk
20
24.10
26.2
ice cream, cheese
and specialist
10
46.60
44.2
This suggests some form of specialization in contracting. It is rational for most of those
that are using relatively small amounts of milk on an infrequent basis not to invest in
35
contract support measures and direct linkages with farmers where they can form a stable
relationship with a suitable wholesaler or other dairy.
Analyzing both liquid milk processors and specialty dairies by country (Table 22) it is
evident that contracting is most extensively developed in Moldova and Armenia. This can
be discerned both in terms of the share of raw materials sourced using contracts and the
mean number of contract support measures employed. In Moldova this may reflect the
higher level of Western-FDI and the previously discussed linkage between Western FDI
and contracting (Table 12). In Armenia the relatively high level of contracting cannot be
linked directly to FDI as all of the dairies in this country are owned by domestic
investors. However a relatively high proportion of Armenian output is exported and a
significant correlation between exporting and the mean number of contract support
measures was found for the full sample (Table 13).
Table 22: Comparison of Ownership and Contracting in Dairy Sector Only (both liquid milk and
specialty dairies) by country
Armenia
Ownership and
Exports
% of cap. privately
owned
% of capital owned by
Western foreign
investors
% of output sold on the
domestic market
Contracting
% of raw material
bought using farm
contracts (2003)
Mean number of
contract support
measures employed
% of supply from small
farms (both spot &
cont.)
Georgia
Moldova
Russia
Ukraine
Total
100.0
80.0
43.3
91.3
100.0
87.7
0.0
20.0
55.0
0.0
0.0
10.0
64.2
100.0
92.5
95.4
99.7
90.7
69.2
42.5
81.3
58.0
27.2
52.0
5.67
3.50
5.25
2.43
1.56
3.33
23.66
17.50
71.25
29.29
28.89
32.07
36
Appendix 4: Interview Questionnaire
Section A: Background Information (some of this may be completed prior to the interview)
1.1
What is the nature of the enterprise? (e.g. dairy, slaughterhouse).
________________________________________________________
1.2
What percentage of the enterprise, if any, is owned by the following?
a) private domestic investors?
b) co-operative(s)
b) state
d) foreign investors
________%
________%
________%
________%
If there are foreign investors, detail:
a) country of origin____________________
b) Year first foreign capital invested __________
c) Year foreign investor became majority owner (if applicable) _______
Useful to give brief details on main owners and recent changes
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
1.3
If the enterprise was privatised, in what year did this occur? ________
1.4
What percentage of your final value of goods do you supply to:
Domestic markets
______%
Other countries in Central and Eastern Europe / former Soviet Union ______%
EU countries
______%
Rest of the world
______%
(Check adds up to 100%!)
1.5
For the following years detail numbers employed and turnover
1997
1999
2001
2003
Number of people
employed in the
company
Turnover (USD),
(approx. if no
accurate figures)
37
Section B: Investments
2.1
Thinking about capital investment in the agri-food sector, what have been the company's two
most recent significant investments?
Investment 1 (detail nature of investment and rough value)
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
Investment 2 (detail nature of investment and rough value)
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
2.2
What was the rationale for these two investments? (as prompts may want to discuss the
following possible motives: improve technology, reduce costs, meet high quality standards, adjust to
changing market trends etc.)
Investment 1
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
Investment 2
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
2.3
Where are the investments located (i.e. particular towns, countries)?
Investment 1 ______________________________
Investment 2 ______________________________
2.4
What were the rationales for choosing these particular locations (as prompts may want to
discuss the potential role of market access, past investment, skilled labour, cheap labour etc.)
Investment 1
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
38
Investment 2
__________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
2.5
Has the company in the last 3 years abandoned any planned investments?
Yes or No? _________
If yes, discuss reasons for this
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
Section 3: Contracting and Procurement
3.1
For the following years what percentage of agricultural raw materials
supplied to the company were obtained through the following ways? (columns
should add to 100%, do not fill in shaded area)
1997
1999
Spot markets
(no contracts)
with farms
- farms with less
than 5 animals / 1
ha*
- farms with more
than 5 animals / 1
ha
Contracts with
farmers
- farms with less
than 5 animals / 1
ha
- farms with more
than 5 animals / 1
ha
Supplied by
company’s own
farms
Other agents /
intermediaries
*(if livestock and dairy use animal figures, if crops use hectares [ha])
2001
2003
39
3.2
For the following years, how many suppliers were you dealing with for each of the possible
procurement channels (if livestock and dairy use animal figures, if crops use hectares [ha])
1997
1999
2001
2003
By spot markets
with farms (no
contracts)
- farms with less
than 5 animals / 1
ha
- farms with more
than 5 animals / 1
ha
Contracts with
farmers
- farms with less
than 5 animals / 1
ha
- farms with more
than 5 animals / 1
ha
Other agents /
intermediaries
3.3
For the following years, what have been the average yields of farmers that supply you under the
following arrangements? (Yields: for example litres of milk per cow, for crops tonnes per ha)
1997
1999
2001
2003
Farms that supply
via spot markets
(no contracts)
- with less than 5
animals / 1 ha
- with more than 5
animals / 1 ha
Farms that
processor has
contracts with
- with less than 5
animals / 1 ha
- with more than 5
animals / 1 ha
40
3.4
What have been the main reasons behind the change in the number of suppliers?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
3.5
How and why is the number of suppliers likely to change in the future?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
3.6
How does the net cost of agricultural raw materials sourced under contracts compare with
(a) spot markets _________+ /- % (for example 10% higher)
b) production on own farms (if any) _________+ /- %
3.7
If appropriate, what are the main reasons for differences in the costs of agricultural raw
materials sourced through spot markets, under contract and for own production?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
41
3.8 Considering your contracts with farms, do you offer any of the following types of support?
Possible Support Measure Offer yes If yes
or no
Year
Outline motivation for
% of
% of
introducd introduction and main
farms
farms
features of support at time dealt with dealt with
of introduction (terms,
to which to which
conditions etc.)
support support
offered in currently
first year offered
introducd
How and why
has use of
measure
changed since
first introduced?
Is there a min. What has been the What has been the
size of farms to impact of the
impact of the support on
which support support measure on the quality of farm
is offered? If the quantity
production? (obtain
yes give: ha/ produced by farms? figures for average %
no of livestock (obtain figure for change in output
etc.
average change in meeting (a) highest
yields)
grade standards and (b)
minimum standards)
Credit
Physical Inputs (e.g.
seeds, feed, including prefinancing feed etc.)
Machinery
Transportation
Agronomic Support
42
Possible Support Measure Offer yes If yes
or no
Year
Outline motivation for
introducd introduction and main
features of support at time
of introduction (terms,
conditions etc.)
% of
% of
farms
farms
dealt with dealt with
to which to which
support support
offered in currently
first year offered
introducd
How and why
has use of
measure
changed since
first introduced?
Is there a min. What has been the What has been the
size of farms to impact of the
impact of the support on
which support support measure on the quality of farm
is offered? If the quantity
production? (obtain
yes give: ha/ produced by farms? figures for average %
no of livestock (obtain figure for change in output
etc
average change in meeting (a) highest
yields)
grade standards and (b)
minimum standards)
Veterinary Support
Business and financial
management support
Harvest & handling
support
Farm loan guarantees
(given by processor to
banks)
Investment loans
43
Possible Support Measure Offer yes If yes
or no
Year
Outline motivation for
introducd introduction and main
features of support at time
of introduction (terms,
conditions etc.)
% of
% of
farms
farms
dealt with dealt with
to which to which
support support
offered in currently
first year offered
introducd
How and why
has use of
measure
changed since
first introduced?
Is there a min. What has been the What has been the
size of farms to impact of the
impact of the support on
which support support measure on the quality of farm
is offered? If the quantity
production? (obtain
yes give: ha/ produced by farms? figures for average %
no of livestock (obtain figure for change in output
etc
average change in meeting (a) highest
yields)
grade standards and (b)
minimum standards)
Specialised storage
Quality control
Market access
Prompt payments
Guaranteed prices
44
3.9
For the following years, what percentages of total raw material / supply costs were accounted
for by transaction costs? (costs of negotiation, monitoring and enforcement of contracts with
suppliers)
1997
1999
2001
2003
Transaction costs
as % of total
supply costs
3.10
What investments, if any, has the company made to better monitor the quality of supply and
contract enforcement? Detail nature of investment(s) and year made.
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
3.11
In the following years, what percentage of contracts were not completed / broken by
farmers?
1997 _____%
1999 _____%
2001 _____%
2003 _____%
3.12
What are the main reasons for contract breaches?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
3.13
What do you do when farmers break the terms of contracts? (Discuss informal means, penalties,
legal action etc.).
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
45
Section 4: DAIRY SPECIFIC QUESTIONS (i.e. only to be asked in dairy cases)
4.1. Do you test milk and adjust payments for the following?
Test milk Since
Adjust
on
when?
payments
purchase (year)
based on
(yes / no)
level (yes /
no)
a) Fat content
b) Cell content
c) Germ content
d) Milk consistency
e) Dry defatted residium
f) Protein content
Since
when?
(year)
Motivation for
introduction (e.g
competition, need for
better quality)
4.2 For the following years please indicate what percentage of the milk you procured fell into the
following categories? (totals for each year should add up to 100%).
Milk quality
1997
% of milk
delivered
1999
% of milk
delivered
2001
% of milk
delivered
2003
% of milk
delivered
Extra class
1st class
2nd class
Rejected / unusable
Section 5: NON-DAIRY QUESTIONS (i.e. only to be asked in none dairy cases)
5.1. For what attributes, if any do you test for raw material quality and / or adjust payments based
on assessments (for example meat –fat content; grapes – sugar content)?
Attribute (e.g. sugar content, fat Test on
Since
Adjust
Since
Motivation for
content)
purchase when?
payments
when?
introduction (e.g
(yes / no) (year)
based on
(year)
competition, need for
level (yes /
better quality)
no)
a)
b)
c)
d)
e)
f)
5.2 For the following years please indicate what percentage of agricultural raw materials (e.g. grapes,
sugar beat) that you procured fell into the following categories? (totals for each year should add up
to 100%).
1997
1999
2001
2003
% of delivered
% of delivered
% of delivered
% of delivered
Premium quality
Acceptable quality
Rejected / unusable
46
Section 6: Opinions on Business Development and Government Intervention
6.1
What do you see as the main current constraints faced by your business?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
6.2
What strategies / measures is the company taking to overcome these constraints?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
6.3
What future constraints and opportunities do you expect to be faced by your business? How
do you expect to deal with these future issues?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
6.4
6.5
What future do you foresee for small (individual) farms in this country? Discuss reasons.
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
What future do you foresee for corporate farms (companies, transformed collective farms
etc.) in this country? Discuss reasons.
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
47
6.6
Does current national government policy present any problems for your business? If yes,
discuss main features. (dig beyond simple answers for underlying issues)
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
6.7
What changes to national government policy would improve the performance of your
enterprise? Why do you think such measures would be beneficial?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
6.8
What would you see as the main role to be played by international agencies such as the
World Bank?
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
6.9
Additional points / notes
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
48